arXiv subjects

Konstantin Zaitsev

Publications and source records attributed to Konstantin Zaitsev.

8 recordsLinked to original sources

arXiv2026

Insecurity of Measurement-Device-Independent Quantum Key Distribution

The security of practical quantum key distribution (QKD) systems is fundamentally constrained by vulnerabilities of single-photon detectors. Measurement-device-independent quantum key distribution (MDI-QKD) was proposed to remove this limitation by allowing all measurements to be performed by a completely untrusted party, under the assumption that the measurement node can be treated as adversarial but does not compromise the security guarantees of the protocol. Here we show that this assumption is insufficient under realistic adversarial control of the measurement device. We present an attack in which an adversary exploits active control of the measurement node (Charlie) to obtain significant information about the secret key. The attack enables recovery of up to 70\% of the sifted key while introducing only 5.6\% quantum bit error rate. Unlike previously reported attacks targeting specific implementations of MDI-QKD, our results demonstrate a limitation of the standard security model underlying the protocol. These findings indicate that additional constraints on the measurement-device independence assumption, or refined security analyses incorporating stronger adversarial capabilities, are required to ensure the security of MDI-QKD in realistic scenarios.

quant-ph↗

arXiv2026

Energy-time attack on detectors in quantum key distribution

Quantum key distribution is unbreakable in theory but may be hacked via imperfections in its hardware implementations. While many imperfections have been mitigated by countermeasures and advanced security proofs, several remain unsolved. One of these is a superlinear behaviour in single-photon detectors, when the click probability rises faster with the photon number of an incoming light pulse than expected from individual independent photon detections. Here we test an avalanche single-photon detector sinusoidally-gated at 312.5 MHz for superlinearity. Its click probability is moderately superlinear. However, we notice that the click timing depends strongly on the incoming pulse energy. The click occurs progressively earlier, shifting more than 2 ns as the energy rises over a wide 50-dB range. An attacker might use this energy-time effect to conditionally toggle the click between adjacent key bit slots, violating an implicit assumption in the security proofs and rendering them inapplicable. We propose two attacks that exploit this flaw.

quant-ph↗

arXiv2026

An Annotation Scheme and Classifier for Personal Facts in Dialogue

The advancement of Large Language Models (LLMs) has enabled their application in personalized dialogue systems. We present an extended annotation scheme for personal fact classification that addresses limitations in existing approaches, particularly PeaCoK. Our scheme introduces new categories (Demographics, Possessions) and attributes (Duration, Validity, Followup) that enable structured storage, quality filtering, and identification of facts suitable for dialogue continuation. We manually annotated 2,779 facts from Multi-Session Chat and trained a multi-head classifier based on transformer encoders. Combined with the Gemma-300M encoder, the classifier achieves $81.6 \pm 2.6$\% macro F1, outperforming all few-shot LLM baselines (best: GPT-5.4-mini, 72.92\%) by nearly 9 percentage points while requiring substantially fewer computational resources. Error analysis reveals persistent challenges in semantic boundary disambiguation, temporal aspect interpretation, and pragmatic reasoning for followup assessment. The dataset\footnotemark[1] and classifier\footnotemark[2] are publicly available.

cs.CL↗

arXiv2024

Enhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approach

In recent years, Large Language Models (LLMs) gain considerable attention for their potential to enhance personalized experiences in virtual assistants and chatbots. A key area of interest is the integration of personas into LLMs to improve dialogue naturalness and user engagement. This study addresses the challenge of persona classification, a crucial component in dialogue understanding, by proposing a framework that combines text embeddings with Graph Neural Networks (GNNs) for effective persona classification. Given the absence of dedicated persona classification datasets, we create a manually annotated dataset to facilitate model training and evaluation. Our method involves extracting semantic features from persona statements using text embeddings and constructing a graph where nodes represent personas and edges capture their similarities. The GNN component uses this graph structure to propagate relevant information, thereby improving classification performance. Experimental results show that our approach, in particular the integration of GNNs, significantly improves classification performance, especially with limited data. Our contributions include the development of a persona classification framework and the creation of a dataset.

cs.CL↗

arXiv2024

Preparing a commercial quantum key distribution system for certification against implementation loopholes

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system's robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarisation encoding, qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and post-processing. We analyse the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure.

quant-ph↗

arXiv2024

Exploring the Effectiveness of Methods for Persona Extraction

The paper presents a study of methods for extracting information about dialogue participants and evaluating their performance in Russian. To train models for this task, the Multi-Session Chat dataset was translated into Russian using multiple translation models, resulting in improved data quality. A metric based on the F-score concept is presented to evaluate the effectiveness of the extraction models. The metric uses a trained classifier to identify the dialogue participant to whom the persona belongs. Experiments were conducted on MBart, FRED-T5, Starling-7B, which is based on the Mistral, and Encoder2Encoder models. The results demonstrated that all models exhibited an insufficient level of recall in the persona extraction task. The incorporation of the NCE Loss improved the model's precision at the expense of its recall. Furthermore, increasing the model's size led to enhanced extraction of personas.

cs.CL↗

arXiv2023

Security boundaries of an optical power limiter for protecting quantum key distribution systems

Unauthorized light injection has always been a vital threat to the practical security of a quantum key distribution (QKD) system. An optical power limiter (OPL) based on the thermo-optical defocusing effect has been proposed and implemented, limiting the injected hacking light. As a hardware countermeasure, the performance of the OPL under various light-injection attacks shall be tested to clarify the security boundary before being widely deployed. To investigate the OPL's security boundary in quantum cryptography, we comprehensively test and analyse the behavior of OPL under continuous-wave (c.w.) light-injection attacks and pulse illumination attacks with pulses' repetition rate at $0.5$-$\hertz$, $40$-$\mega\hertz$, and $1$-$\giga\hertz$. The testing results illuminate the security boundary of the OPL, which allows one to properly employ the OPL in the use cases. The methodology of testing and analysis proposed here is applicable to other power-limitation components in a QKD system.

quant-ph↗

arXiv2023

Automated verification of countermeasure against detector-control attack in quantum key distribution

Attacks that control single-photon detectors in quantum key distribution using tailored bright illumination are capable of eavesdropping the secret key. Here we report an automated testbench that checks the detector's vulnerabilities against these attacks. We illustrate its performance by testing a free-running detector that includes a rudimentary countermeasure measuring an average photocurrent. While our testbench automatically finds the detector to be controllable in a continuous-blinding regime, the countermeasure registers photocurrent significantly exceeding that in a quantum regime, thus revealing the attack. We then perform manually a pulsed blinding attack, which controls the detector intermittently. This attack is missed by the countermeasure in a wide range of blinding pulse durations and powers, still allowing to eavesdrop the key. We make recommendations for improvement of both the testbench and countermeasure.

physics.ins-det↗